Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x22a1a96c160>
---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
D:\python\envs\tmp\lib\site-packages\IPython\core\formatters.py in __call__(self, obj)
    339                 pass
    340             else:
--> 341                 return printer(obj)
    342             # Finally look for special method names
    343             method = get_real_method(obj, self.print_method)

D:\python\envs\tmp\lib\site-packages\IPython\core\pylabtools.py in <lambda>(fig)
    236 
    237     if 'png' in formats:
--> 238         png_formatter.for_type(Figure, lambda fig: print_figure(fig, 'png', **kwargs))
    239     if 'retina' in formats or 'png2x' in formats:
    240         png_formatter.for_type(Figure, lambda fig: retina_figure(fig, **kwargs))

D:\python\envs\tmp\lib\site-packages\IPython\core\pylabtools.py in print_figure(fig, fmt, bbox_inches, **kwargs)
    120 
    121     bytes_io = BytesIO()
--> 122     fig.canvas.print_figure(bytes_io, **kw)
    123     data = bytes_io.getvalue()
    124     if fmt == 'svg':

D:\python\envs\tmp\lib\site-packages\matplotlib\backend_bases.py in print_figure(self, filename, dpi, facecolor, edgecolor, orientation, format, **kwargs)
   2214                     orientation=orientation,
   2215                     dryrun=True,
-> 2216                     **kwargs)
   2217                 renderer = self.figure._cachedRenderer
   2218                 bbox_inches = self.figure.get_tightbbox(renderer)

D:\python\envs\tmp\lib\site-packages\matplotlib\backends\backend_agg.py in print_png(self, filename_or_obj, *args, **kwargs)
    505 
    506     def print_png(self, filename_or_obj, *args, **kwargs):
--> 507         FigureCanvasAgg.draw(self)
    508         renderer = self.get_renderer()
    509         original_dpi = renderer.dpi

D:\python\envs\tmp\lib\site-packages\matplotlib\backends\backend_agg.py in draw(self)
    428             if toolbar:
    429                 toolbar.set_cursor(cursors.WAIT)
--> 430             self.figure.draw(self.renderer)
    431         finally:
    432             if toolbar:

D:\python\envs\tmp\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
     53                 renderer.start_filter()
     54 
---> 55             return draw(artist, renderer, *args, **kwargs)
     56         finally:
     57             if artist.get_agg_filter() is not None:

D:\python\envs\tmp\lib\site-packages\matplotlib\figure.py in draw(self, renderer)
   1297 
   1298             mimage._draw_list_compositing_images(
-> 1299                 renderer, self, artists, self.suppressComposite)
   1300 
   1301             renderer.close_group('figure')

D:\python\envs\tmp\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
    136     if not_composite or not has_images:
    137         for a in artists:
--> 138             a.draw(renderer)
    139     else:
    140         # Composite any adjacent images together

D:\python\envs\tmp\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
     53                 renderer.start_filter()
     54 
---> 55             return draw(artist, renderer, *args, **kwargs)
     56         finally:
     57             if artist.get_agg_filter() is not None:

D:\python\envs\tmp\lib\site-packages\matplotlib\axes\_base.py in draw(self, renderer, inframe)
   2435             renderer.stop_rasterizing()
   2436 
-> 2437         mimage._draw_list_compositing_images(renderer, self, artists)
   2438 
   2439         renderer.close_group('axes')

D:\python\envs\tmp\lib\site-packages\matplotlib\image.py in _draw_list_compositing_images(renderer, parent, artists, suppress_composite)
    136     if not_composite or not has_images:
    137         for a in artists:
--> 138             a.draw(renderer)
    139     else:
    140         # Composite any adjacent images together

D:\python\envs\tmp\lib\site-packages\matplotlib\artist.py in draw_wrapper(artist, renderer, *args, **kwargs)
     53                 renderer.start_filter()
     54 
---> 55             return draw(artist, renderer, *args, **kwargs)
     56         finally:
     57             if artist.get_agg_filter() is not None:

D:\python\envs\tmp\lib\site-packages\matplotlib\image.py in draw(self, renderer, *args, **kwargs)
    553         else:
    554             im, l, b, trans = self.make_image(
--> 555                 renderer, renderer.get_image_magnification())
    556             if im is not None:
    557                 renderer.draw_image(gc, l, b, im)

D:\python\envs\tmp\lib\site-packages\matplotlib\image.py in make_image(self, renderer, magnification, unsampled)
    780         return self._make_image(
    781             self._A, bbox, transformed_bbox, self.axes.bbox, magnification,
--> 782             unsampled=unsampled)
    783 
    784     def _check_unsampled_image(self, renderer):

D:\python\envs\tmp\lib\site-packages\matplotlib\image.py in _make_image(self, A, in_bbox, out_bbox, clip_bbox, magnification, unsampled, round_to_pixel_border)
    417 
    418                 mask = np.empty(A.shape, dtype=np.float32)
--> 419                 if A.mask.shape == A.shape:
    420                     # this is the case of a nontrivial mask
    421                     mask[:] = np.where(A.mask, np.float32(np.nan),

AttributeError: 'numpy.ndarray' object has no attribute 'mask'
<matplotlib.figure.Figure at 0x22a171a8908>
In [3]:
# In my environment, matplotlib cant show grayscale image.
import numpy as np
from PIL import Image
img = helper.images_square_grid(mnist_images, 'L')
rgbimg = Image.new("RGB", img.size)
rgbimg.paste(img)
pyplot.imshow(rgbimg)
Out[3]:
<matplotlib.image.AxesImage at 0x22a1ab2dac8>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [4]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[4]:
<matplotlib.image.AxesImage at 0x22a1aba9a90>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [5]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.1.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [6]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    real_input = tf.placeholder(tf.float32, shape=(None, image_width, image_height, image_channels), name='real_input')
    z_input = tf.placeholder(tf.float32, shape=(None, z_dim), name='z_input')
    learningrate = tf.placeholder(tf.float32, name='learningrate')
    return real_input, z_input, learningrate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [7]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    alpha = 0.1
    with tf.variable_scope('discriminator', reuse=reuse):
        # Input layer is 28x28xchan
        
        conv1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        #relu1 = tf.nn.relu(conv1)
        relu1 = tf.maximum(alpha * conv1, conv1)
        # 14x14x64
        
        conv2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        conv2 = tf.layers.batch_normalization(conv2, training=True)
        #relu2 = tf.nn.relu(conv2)
        relu2 = tf.maximum(alpha * conv2, conv2)
        # 7x7x128
        
        conv3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='same')
        conv3 = tf.layers.batch_normalization(conv3, training=True)
        #relu3 = tf.nn.relu(conv3)
        relu3 = tf.maximum(alpha * conv3, conv3)
        # 4x4x256

        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        output = tf.sigmoid(logits)
        
        return output, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [8]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    alpha = 0.1
    with tf.variable_scope('generator', reuse=not is_train):
        fc1 = tf.layers.dense(z, (3*3*512))
        fc1 = tf.reshape(fc1, (-1, 3, 3, 512))
        fc1 = tf.layers.batch_normalization(fc1, training=is_train)
        #relu1 = tf.nn.relu(fc1)
        relu1 = tf.maximum(alpha * fc1, fc1)
        # 4x4x512

        conv_trans1 = tf.layers.conv2d_transpose(relu1, 256, 3, strides=2, padding='valid')
        conv_trans1 = tf.layers.batch_normalization(conv_trans1, training=is_train)
        #relu2 = tf.nn.relu(conv_trans1)
        relu2 = tf.maximum(alpha * conv_trans1, conv_trans1)
        # 7x7x256

        conv_trans2 = tf.layers.conv2d_transpose(relu2, 128, 5, strides=2, padding='same')
        conv_trans2 = tf.layers.batch_normalization(conv_trans2, training=is_train)
        #relu3 = tf.nn.relu(conv_trans2)
        relu3 = tf.maximum(alpha * conv_trans2, conv_trans2)
        # 14x14x128

        conv_trans3 = tf.layers.conv2d_transpose(relu3, out_channel_dim, 5, strides=2, padding='same')
        out        = tf.tanh(conv_trans3)
        # 28x28xchan
        
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [9]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim, is_train=True)
    d_model_real, d_logits_real = discriminator(input_real, reuse=False)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_logits_fake,
            labels=tf.ones_like(d_logits_fake)))
    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_logits_real, 
            labels=tf.ones_like(d_logits_real)*(1 - 0.1)))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(
            logits=d_logits_fake, 
            labels=tf.zeros_like(d_logits_fake)))

    d_loss = d_loss_real + d_loss_fake
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [10]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    t_vars = tf.trainable_variables()
    g_vars = [v for v in t_vars if v.name.startswith('generator')]
    d_vars = [v for v in t_vars if v.name.startswith('discriminator')]
    
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        g_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
        d_opt = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        
    return d_opt, g_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [11]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()
In [12]:
import numpy as np
from PIL import Image

def show_generator_output2(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    rgbimg = Image.new("RGB", images_grid.size)
    rgbimg.paste(images_grid)
    
    pyplot.imshow(rgbimg)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [13]:
losses = []
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    #saver = tf.train.Saver()
    global losses
    losses = []
    image_channels = 3 if data_image_mode == 'RGB' else 1
    real_input_, z_input_, learning_rate_ = model_inputs(data_shape[1], data_shape[2], image_channels, z_dim)
    d_loss, g_loss = model_loss(real_input_, z_input_, image_channels)
    d_opt, g_opt   = model_opt(d_loss, g_loss, learning_rate_, beta1)
    steps = 0
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                steps += 1
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                _ = sess.run(d_opt, feed_dict={real_input_: batch_images, z_input_: batch_z, learning_rate_: learning_rate})
                _ = sess.run(g_opt, feed_dict={real_input_: batch_images, z_input_: batch_z, learning_rate_: learning_rate})
                
                if steps % 50 == 0:
                    train_d_loss = d_loss.eval({real_input_: batch_images, z_input_: batch_z, learning_rate_: learning_rate})
                    train_g_loss = g_loss.eval({real_input_: batch_images, z_input_: batch_z, learning_rate_: learning_rate})
                    print("Epoch={}/{} step={} ".format(epoch_i, epoch_count, steps), 
                          "D-Loss={:.4F}".format(train_d_loss),
                          "G-Loss={:.4F}".format(train_g_loss))
                    losses.append((train_d_loss, train_g_loss))
                    show_generator_output2(sess, 25, z_input_, image_channels, data_image_mode)

    #saver.save(sess, './checkpoints/generator.ckpt')

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [14]:
batch_size = 128
z_dim = 100
learning_rate = 0.0003
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch=0/2 step=50  D-Loss=0.6706 G-Loss=2.0262
Epoch=0/2 step=100  D-Loss=1.0201 G-Loss=4.7378
Epoch=0/2 step=150  D-Loss=0.5185 G-Loss=2.8812
Epoch=0/2 step=200  D-Loss=0.7984 G-Loss=1.2552
Epoch=0/2 step=250  D-Loss=0.6641 G-Loss=1.9930
Epoch=0/2 step=300  D-Loss=0.5355 G-Loss=2.0195
Epoch=0/2 step=350  D-Loss=0.5006 G-Loss=2.1710
Epoch=0/2 step=400  D-Loss=0.4802 G-Loss=2.2848
Epoch=0/2 step=450  D-Loss=0.4475 G-Loss=2.7843
Epoch=1/2 step=500  D-Loss=0.4683 G-Loss=2.3381
Epoch=1/2 step=550  D-Loss=0.4339 G-Loss=2.9486
Epoch=1/2 step=600  D-Loss=0.3758 G-Loss=3.5053
Epoch=1/2 step=650  D-Loss=0.5128 G-Loss=2.2309
Epoch=1/2 step=700  D-Loss=0.4182 G-Loss=2.7566
Epoch=1/2 step=750  D-Loss=0.4381 G-Loss=3.9951
Epoch=1/2 step=800  D-Loss=0.5960 G-Loss=2.3384
Epoch=1/2 step=850  D-Loss=0.9611 G-Loss=1.3816
Epoch=1/2 step=900  D-Loss=0.5462 G-Loss=2.0450

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [15]:
batch_size = 128
z_dim = 150
learning_rate = 0.0001
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch=0/1 step=50  D-Loss=0.4403 G-Loss=2.9264
Epoch=0/1 step=100  D-Loss=0.6263 G-Loss=1.6353
Epoch=0/1 step=150  D-Loss=0.4694 G-Loss=3.1152
Epoch=0/1 step=200  D-Loss=0.5264 G-Loss=4.4773
Epoch=0/1 step=250  D-Loss=0.4732 G-Loss=4.5171
Epoch=0/1 step=300  D-Loss=0.6589 G-Loss=1.7812
Epoch=0/1 step=350  D-Loss=0.5124 G-Loss=3.4360
Epoch=0/1 step=400  D-Loss=0.4638 G-Loss=3.2171
Epoch=0/1 step=450  D-Loss=1.0860 G-Loss=0.8304
Epoch=0/1 step=500  D-Loss=0.5759 G-Loss=2.1752
Epoch=0/1 step=550  D-Loss=1.0357 G-Loss=3.5604
Epoch=0/1 step=600  D-Loss=0.6481 G-Loss=1.9221
Epoch=0/1 step=650  D-Loss=0.5897 G-Loss=2.3020
Epoch=0/1 step=700  D-Loss=0.5592 G-Loss=2.3740
Epoch=0/1 step=750  D-Loss=0.4578 G-Loss=2.8820
Epoch=0/1 step=800  D-Loss=0.4807 G-Loss=2.3804
Epoch=0/1 step=850  D-Loss=0.4838 G-Loss=2.4394
Epoch=0/1 step=900  D-Loss=0.4343 G-Loss=2.9342
Epoch=0/1 step=950  D-Loss=0.6063 G-Loss=1.7960
Epoch=0/1 step=1000  D-Loss=0.4342 G-Loss=3.0768
Epoch=0/1 step=1050  D-Loss=0.5079 G-Loss=2.4197
Epoch=0/1 step=1100  D-Loss=0.5676 G-Loss=2.0105
Epoch=0/1 step=1150  D-Loss=0.4934 G-Loss=3.0159
Epoch=0/1 step=1200  D-Loss=0.5573 G-Loss=2.5530
Epoch=0/1 step=1250  D-Loss=0.4257 G-Loss=3.1874
Epoch=0/1 step=1300  D-Loss=0.6865 G-Loss=1.5310
Epoch=0/1 step=1350  D-Loss=0.5822 G-Loss=2.2246
Epoch=0/1 step=1400  D-Loss=0.5065 G-Loss=2.5355
Epoch=0/1 step=1450  D-Loss=0.4333 G-Loss=3.1657
Epoch=0/1 step=1500  D-Loss=0.4087 G-Loss=3.3374
Epoch=0/1 step=1550  D-Loss=0.4528 G-Loss=2.7556
In [16]:
import matplotlib.pyplot as plt
fig, ax = plt.subplots()
losses = np.array(losses)
plt.plot(losses.T[0], label='Discriminator', alpha=0.5)
plt.plot(losses.T[1], label='Generator', alpha=0.5)
plt.title("Training Losses")
plt.legend()
Out[16]:
<matplotlib.legend.Legend at 0x22bfdda2c50>

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.